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Unconstrained Face Detection and Open-Set Face Recognition Challenge

: Gunther, M.; Hu, P.; Herrmann, C.; Chan, C.H.; Jiang, M.; Yang, S.; Dhamija, A.R.; Ramanan, D.; Beyerer, J.; Kittler, J.; Jazaery, M.A.; Nouyed, M.I.; Guo, G.; Stankiewicz, C.; Boult, T.E.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE International Joint Conference on Biometrics, IJCB 2017 : 1-4 October 2017, Denver, Colorado, USA; Proceedings
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2017
ISBN: 978-1-5386-1124-1
ISBN: 978-1-5386-1125-8
International Joint Conference on Biometrics (IJCB) <2017, Denver/Colo.>
Conference Paper
Fraunhofer IOSB ()

Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.